The literature in deep learning features a large amount of work in the estimation of uncertainty.
The approaches are often tied to the process for which they predict error models.
Interesting works in domains similar to ours have been proposed, mainly tackling the Visual Odometry (VO) case.
As ICP, VO is a well-known family of techniques which estimate a rigid transformation between two consecutive frames for the monocular or stereovision cases.
End-to-end methods perform frame to frame motion estimation [1]} and covariance prediction [2]}.
Both estimates are in this case delegated to neural networks, and do not rely on any geometrical pipeline.
The works in [3]}, [4]} respectively show covariance and joint bias-covariance estimation to the geometric implementation of VO.
